DATASET – FTIR-derived soil degradation indices and stochastic modelling of organic matter–sediment dynamics in a Mediterranean watershed: a Northern Apennines case study

Supplementary Information datasets for the paper: 'FTIR-derived soil degradation indices and stochastic modelling of organic matter–sediment dynamics in a Mediterranean watershed: a Northern Apennines case study' published in PLOS ONE (https://doi.org/10.1371/journal.pone.0330252).The uploaded files represent the datasets of 'target' and 'feature' variables related to the three experiments performed in the above-mentioned study, along with the related raster and shape file data (feature variables), contributing areas, and the location of the sampling points. The dataset was compiled by Dr. Manuel La Licata and constitutes the fundamental data foundation that enabled the development of the study. The data related to the A/B, A/D, B/D, and C/D indices (i.e., indices characterizing Soil Organic Matter of the 73 samples) were collected and analyzed during the internship of Dr. Manuel La Licata at the Leibniz Centre for Agricultural Landscape Research (ZALF), in the FTIR spectroscopy laboratory under the guidance and supervision of Dr. Ellerbrock Ruth and the coordination of Dr. Jörg Schaller. The data for the remaining variables were independently acquired by Dr. La Licata through statistical GIS analyses. The datasets from the three experiments were subsequently analyzed by Dr. Odunayo Adeniyi using a Random Forest machine learning approach. Nisha Bhattarai and Dr. Alberto Bosino participated in the discussion of data as well as the interpretation of the results, along with all the other Contributors. Natalie Papke supported sample preparation in the laboratory. The published work was supervised by the group leader and coordinator Prof. Michael Maerker, who contributed also to the interpretation and discussion of the results. For further details on the procedures for data collection, acquisition, organization, and analysis, please refer to the Materials and Methods section of the published article.The code of the Random Forest model implemented in this study is available at the following link: https://github.com/Odunayo3/SoilDegradationModeling.gitArticle citation:La Licata M, Adeniyi OD, Ellerbrock RH, Bhattarai N, Bosino A, Papke N, et al. (2025) FTIR-derived soil degradation indices and stochastic modelling of organic matter–sediment dynamics in a Mediterranean watershed: A Northern Apennines case study. PLoS One 20(8): e0330252. https://doi.org/10.1371/journal.pone.0330252._________________________________________Abstract of the paper In this study we explored the relationships between Soil Organic Matter (SOM) properties, serving as potential indicators of soil degradation and erosion, and environmental, geomorphic, and hydrological characteristics in an agricultural-forested Mediterranean watershed. SOM composition of fluvial sediments sampled across the watershed was analysed using FTIR spectroscopy to calculate FTIR-based proxies for the relative hydrophobicity of SOM, Cation Exchange Capacity (CEC), and organic-matter-cation associations. To investigate geospatial relationships between SOM composition influencing erosion susceptibility and the factors driving its variability at the watershed scale, such as terrain characteristics, soil properties, lithological, and LULC data, we used a Random Forest modelling approach. Our findings indicate that the size and configuration of the contributing areas associated with the sampling points played a crucial role in interpreting the relationships between SOM composition and environmental factors. Oak, hornbeam, and chestnut forests influence hydrophobic organic matter accumulation, making soils more prone to water erosion, where clay content potentially intensifies erosion susceptibility under particular climatic conditions. Moreover, SOM chemical components were spatially linked to sediment dynamics and organic matter connectivity across the watershed, with topographic features such as elevation and channel network base level being key factors. Also, CEC was found to be a potential indicator of soil erosion in geomorphologically active areas. Lastly, carbonate-rich soils appeared to positively influence organic matter-cation associations, potentially enhancing aggregate stability and reducing erosion susceptibility. This study provides significant new insights into the complex relationships between SOM composition, environmental predictors, and soil erosion in Mediterranean watersheds, supporting novel research hypotheses and perspectives from both a scientific and applicative point of view.